我有一个看起来像这样的 pandas DataFrame(示例):
I have a pandas DataFrame that looks like this (sample):
data = {
'start': ['2018-10-29 18:48:46.697000',
'2018-10-29 19:01:10.887000',
'2018-10-22 17:42:24.467000'],
'end': ['2018-10-31 17:56:38.830000',
'2018-11-27 09:31:39.967000',
'2018-11-28 18:33:35.243000' ]
}
df = pd.DataFrame(data)
df['start'] = pd.to_datetime(df['start'])
df['end'] = pd.to_datetime(df['end'])
我的目标是计算 start
和 end
之间的(美国)营业时间,不包括周末和节假日.为此,我正在使用 pandas
的 CustomBusinessDay
功能,如下所示:
My goal is to calculate the (US) business hours between start
and end
, excluding weekends and holidays. For that I am using the CustomBusinessDay
functionality of pandas
as follows:
from pandas.tseries.holiday import USFederalHolidayCalendar
from pandas.tseries.offsets import CustomBusinessDay
us_bd = CustomBusinessDay(calendar=USFederalHolidayCalendar())
len(pd.bdate_range(start=df['start'][2], end=df['end'][2], freq=us_bd))
>> 26
这在工作日方面是正确的(它不包括周末、感恩节和黑色星期五的假期),但我真正想要的是两个时间戳之间的工作时间数.所以当我尝试原生 BH
:
This is correct in terms of business days (it does exclude weekends, and the holidays of Thanksgiving and Black Friday), but what I really want is the number of business hours between the two timestamps. So when I try the native BH
:
len(pd.bdate_range(start=df['start'][2], end=df['end'][2], freq='BH'))
>> 216
这是不正确的,因为它考虑了周末,但不考虑了节假日.所以,我有两个问题:
which is incorrect, because it accounts for weekends, but not for the holidays. So, I have two questions:
当我尝试类似:
df['diff'] = pd.bdate_range(start=df['start'], end=df['end'], freq='BH')
结果是:
TypeError: Can not convert input [...] of type to Timestamp
TypeError: Can not convert input [...] of type to Timestamp
错误消息还包括数组中的整个系列.
The error message also included the whole series in the array.
你应该使用 CustomBusinessHour
和 pd.date_range
而不是 pd.bdate_range
.
You should use CustomBusinessHour
and pd.date_range
instead of pd.bdate_range
.
第二行的小时数应该是 145,因为结束时间是 09:31:39.967
.
The number of hours for your second row should be 145 because endtime is 09:31:39.967
.
us_bh = CustomBusinessHour(calendar=USFederalHolidayCalendar())
df['count'] = df.apply(lambda x: len(pd.date_range(start=x.start, end=x.end, freq= us_bh)),axis=1)
df['diff'] = df.apply(lambda x: pd.date_range(start=x.start, end=x.end, freq= us_bh),axis=1)
print(df)
start end count diff
0 2018-10-29 18:48:46.697 2018-10-31 17:56:38.830 16 DatetimeIndex(['2018-10-30 09:00:00', '2018-10...
1 2018-10-29 19:01:10.887 2018-11-27 09:31:39.967 145 DatetimeIndex(['2018-10-30 09:00:00', '2018-10...
2 2018-10-22 17:42:24.467 2018-11-28 18:33:35.243 200 DatetimeIndex(['2018-10-23 09:00:00', '2018-10...
当您使用 pd.bdate_range
时,diff
列的开始营业时间将 '2018-10-29 09:00:00'
.
And diff
columns start business hour will '2018-10-29 09:00:00'
when you use pd.bdate_range
.
us_bh = CustomBusinessHour(calendar=USFederalHolidayCalendar())
df['count'] = df.apply(lambda x: len(pd.bdate_range(start=x.start, end=x.end, freq= us_bh)),axis=1)
df['diff'] = df.apply(lambda x: pd.bdate_range(start=x.start, end=x.end, freq= us_bh),axis=1)
print(df)
start end count diff
0 2018-10-29 18:48:46.697 2018-10-31 17:56:38.830 16 DatetimeIndex(['2018-10-29 09:00:00', '2018-10...
1 2018-10-29 19:01:10.887 2018-11-27 09:31:39.967 152 DatetimeIndex(['2018-10-29 09:00:00', '2018-10...
2 2018-10-22 17:42:24.467 2018-11-28 18:33:35.243 200 DatetimeIndex(['2018-10-22 09:00:00', '2018-10...
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